Scaling and Trade-offs in Multi-agent Autonomous Systems
For designers of autonomous drone swarms, this work provides a data-driven method to predict performance and make budget-aware design choices, though the results are domain-specific and incremental.
The paper uses agent-based simulations to derive scaling laws for drone swarms in three scenarios, revealing sharp performance boundaries and trade-offs between agent count and platform parameters. The scaling functions enable rapid sizing and algorithm selection for autonomous swarms.
Designing autonomous drone swarms is hampered by a vast design space spanning platform, algorithmic, and numerical-strength choices. We perform large-scale agent-based simulations in three canonical scenarios: swarm-on-swarm battle, cooperative area search with attrition, and pursuit of scattering targets. We demonstrate how dimensional-analysis and data-scaling can be leveraged to collapse performance data onto scaling functions that are mathematically simple, yet counterintuitive and therefore difficult to predict a priori. These scaling laws reveal success-failure boundaries, including sharp break points which we show can be framed as an ``effective swarm size.'' Additionally, we show how this technique can be used to quantify trade-offs between agent count and platform parameters such as velocity, sensing or weapon range, and attrition rate. Furthermore, we show the benefits of embedding an optimal path planning loop within this framework, which can qualitatively improve the scaling laws that govern the outcome. The methods we demonstrate are highly flexible and would enable rapid, budget-aware sizing and algorithm selection for large autonomous swarms.